Integrating Attention Mechanisms in Deep Learning Models for Improved Lung Cancer Detection Using CT Scan Images

Cancer ranks among the top causes of death, primarily due to its tendency to be diagnosed at a late stage and its highly aggressive nature.Lung cancer, in particular, has one of the highest mortality rates due to delayed diagnosis.The purpose of this study is to address this issue by applying cutting-edge deep learning methods for the prompt identification of lung cancer from CT scan images.In particular, various Convolutional Neural Network (CNN) architectures are utilized to distinguish between cancerous and non-cancerous CT scan images.To further enhance classification accuracy, a spatial attention mechanism is integrated into the models, enabling them to focus on relevant image regions.The developed models are assessed using a publicly accessible dataset and assessed based on various performance indicators, including precision, recall, accuracy and the F1-score.The findings highlight the superior performance of the attention-enhanced DenseNet model, achieving significant improvements in classification metrics compared to other architectures.The research supports the improvement of reliable computer-aided diagnosis systems for lung cancer, with a focus on the effectiveness of embedding attention modules within CNNs for medical imaging tasks.